Relation between Convoled Spectra and not convolved Spectra of H20.

In [14]:
### Load modules and Bokeh
# Imports from __future__ in case we're running Python 2
from __future__ import division, print_function
from __future__ import absolute_import, unicode_literals

import numpy as np
import matplotlib.pyplot as plt
from astropy.io import fits

# Seaborn, useful for graphics
import seaborn as sns

# Magic function to make matplotlib inline; other style specs must come AFTER
%matplotlib inline

# Import Bokeh modules for interactive plotting
import bokeh.io
import bokeh.mpl
import bokeh.plotting

# This enables SVG graphics inline.  There is a bug, so uncomment if it works.
%config InlineBackend.figure_formats = {'svg',}

# This enables high resolution PNGs. SVG is preferred, but has problems
# rendering vertical and horizontal lines
#%config InlineBackend.figure_formats = {'png', 'retina'}

# JB's favorite Seaborn settings for notebooks
rc = {'lines.linewidth': 1, 
      'axes.labelsize': 14, 
      'axes.titlesize': 16, 
      'axes.facecolor': 'DFDFE5'}
sns.set_context('notebook', rc=rc)
sns.set_style('darkgrid', rc=rc)

# Set up Bokeh for inline viewing
bokeh.io.output_notebook()
Loading BokehJS ...

Load non convolved telluric

In [15]:
import Obtain_Telluric as obt

tapas_h20 = "tapas_2012-04-07T00-24-03_ReqId_12_No_Ifunction_barydone-NO.ipac"

tapas_h20_data, tapas_h20_hdr = obt.load_telluric("", tapas_h20)
tapas_h20_airmass = float(tapas_h20_hdr["airmass"])

print("Telluric Airmass ", tapas_h20_airmass)
try:
    tapas_h20_respower = int(float((tapas_h20_hdr["respower"])))
except:
    tapas_h20_respower = "Nan"
print("Telluric Resolution Power =", tapas_h20_respower)

Telluric Airmass  1.628051
Telluric Resolution Power = Nan
In [16]:
# Load convolved
In [17]:
#conv_wav, conv_flux = np.loadtxt("Convolved_50000_tapas_allchips.txt", delimiter="'",unpack=True)


conv_wav = np.loadtxt("Convolved_50000_tapas_wavelength_allchips.txt")
conv_flux = np.loadtxt("Convolved_50000_tapas_transmitance_allchips.txt")

print(conv_wav)
print(conv_flux)

[ 2111.78401652  2111.78452     2111.78502349 ...,  2165.23488973
  2165.23541902  2165.23594832]
[ 0.99575302  0.99573294  0.99571264 ...,  0.95180895  0.95180787
  0.95180678]
In [ ]:
In [20]:
orig_flux = np.array([flux for wav, flux in zip(tapas_h20_data[0],tapas_h20_data[1]) if wav in conv_wav])

plt.plot(orig_flux, conv_flux, "o")
plt.title("Affect of Convolution R=50000")
plt.xlabel("Original Flux")
plt.ylabel("Convolved Flux\nR=50000")

bokeh.plotting.show(bokeh.mpl.to_bokeh())
Out[20]:

<Bokeh Notebook handle for In[14]>

In [21]:
plt.plot(tapas_h20_data[0],tapas_h20_data[1])
plt.plot(conv_wav, conv_flux)
plt.xlabel("Wavelenght")
plt.ylabel("Flux")

bokeh.plotting.show(bokeh.mpl.to_bokeh())
Out[21]:

<Bokeh Notebook handle for In[14]>

In [26]:
#Wavelenght density
plt.plot(tapas_h20_data[0][1:],tapas_h20_data[0][1:]-tapas_h20_data[0][:-1])
plt.ylabel("Delta Wavelength")
plt.xlabel("Wavelength (nm)")
plt.title("Distribution of wavelength is not uniform")
bokeh.plotting.show(bokeh.mpl.to_bokeh())
Out[26]:

<Bokeh Notebook handle for In[14]>

In [ ]: